Statistical Analysis of Complex Problem-Solving Process Data: An Event History Analysis Approach.

Yunxiao Chen,Xiaoou Li,Jingchen Liu,Zhiliang Ying

doi:10.3389/fpsyg.2019.00486

Abstract

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill. Individuals' processes of solving crucial complex problems may contain substantial information about their CPS ability. In this paper, we consider the prediction of duration and final outcome (i.e., success/failure) of solving a complex problem during task completion process, by making use of process data recorded in computer log files. Solving this problem may help answer questions like “how much information about an individual's CPS ability is contained in the process data?,” “what CPS patterns will yield a higher chance of success?,” and “what CPS patterns predict the remaining time for task completion?” We propose an event history analysis model for this prediction problem. The trained prediction model may provide us a better understanding of individuals' problem-solving patterns, which may eventually lead to a good design of automated interventions (e.g., providing hints) for the training of CPS ability. A real data example from the 2012 Programme for International Student Assessment (PISA) is provided for illustration.

Highlights

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill of high importance for several outcomes including academic achievement (Wüstenberg et al, 2012) and workplace performance (Danner et al, 2011)
Since the initial model does not take into account the event history, it predicts the students with duration longer than t to have the same success probability
As an early step toward understanding individuals’ complex problem-solving processes, we proposed an event history analysis method for the prediction of the duration and the final outcome of solving a complex problem based on process data

Summary

Introduction

Complex problem-solving (CPS) ability has been recognized as a central 21st century skill of high importance for several outcomes including academic achievement (Wüstenberg et al, 2012) and workplace performance (Danner et al, 2011). Even for individuals who take the same strategy, their actions and time-stamps of the actions may be very different Due to such heterogeneity and complexity, classical regression and multivariate data analysis methods cannot be straightforwardly applied to CPS process data. The dataset was cleaned from the entire released dataset of PISA 2012 It contains 16,872 15-year-old students’ problem-solving processes, where the students were from 42 countries and economies.

Methods

Results

Conclusion